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State and parameter estimation in distributed constrained systems. 2. GA-EKF based sensor placement for a water gas shift reactor
Parham Mobed, Sudhakar Munusamy, Debangsu Bhattacharyya,
Published in American Chemical Society
2017
Volume: 56
   
Issue: 1
Pages: 216 - 224
Abstract
Growing complexity of processes necessitates the use of information from sensors along with first-principles mathematical models to ensure safe and optimal operations. Use of sensors in complex processes requires identifying optimal location of sensors that can maximize information from a process. Classical sensor placement approaches for nonlinear systems that use state estimation schemes usually incorporate linearized models around the steady-state operating point. However, such approaches face difficulties when abnormalities or disturbances drift the system away from the normal operating point. Therefore, use of models that can appropriately track the behavior of the system in the sensor placement framework are of interest. However, the computational complexity of the detailed models makes such approaches intractable. In this work, we develop a sensor placement framework that combines genetic algorithms and the extended Kalman filter to obtain optimal sensor locations. Within this framework, we have investigated the applicability of simplified models by comparing the results of sensor placement for simplified and detailed models. The effect of the simplified models on the estimation accuracy and the optimal sensor network is further evaluated by analyzing the sensitivity to different parameters. Results show that an appropriate simplified model can not only significantly reduce the computational time of the sensor placement algorithm, but also yield a senor network with similar characteristics as the sensor network obtained using the detailed model. Further, information loss in using simplified models in sensor placement may be partially compensated through tuning of the filter parameters, resulting in acceptable, optimal sensor placement solutions. (Graph Presented). © 2016 American Chemical Society.
About the journal
JournalData powered by TypesetIndustrial and Engineering Chemistry Research
PublisherData powered by TypesetAmerican Chemical Society
ISSN08885885
Open AccessNo
Concepts (15)
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    Complex networks
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    Estimation
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    Extended kalman filters
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    Genetic algorithms
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    Sensor networks
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    Water gas shift
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    Computational time
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    CONSTRAINED SYSTEMS
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    Estimation schemes
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    Optimal sensor locations
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    Optimal sensor placement
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    State and parameter estimations
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    STEADY-STATE OPERATING
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    Water gas shift reactors
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    Parameter estimation